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Box 6.1  

Cut-off sampling 

Cut-off sampling is a strategy frequently used by countries to select samples. In that approach, a predetermined threshold is established, with the inclusion in the sample of all units at or above the threshold (selected with certainty). Units below the threshold are not included (zero probability of selection). Cut-off sampling is a common technique in economic surveys in cases in which most economic activity is generated by the largest firms. It can be used to reduce the number of firms required to report in non-benchmark years, thus reducing the burden on a set of firms by their exclusion and reducing processing costs.

        Cut-off sampling generally results in a high degree of coverage among a small number of prospective units, because the distribution of the selection variable, such as production or sales, is concentrated in a small number of large establishments. Limiting the target population enables the exclusion of small enterprises and/or enterprises that are not primarily engaged in providing services. That method is resource efficient and ensures that large firms are included. However, there is no way to determine if firms that fall below the cut-off behave in similar ways to the firms in the survey. 

        Sample frame cut-offs are revaluated regularly. If benchmark surveys are conducted, cut-offs are typically left unchanged between benchmark surveys, and are often increased at each benchmark, for a number of reasons. First, since cut-offs are set in nominal terms, inflation will eventually cause enterprises that did not grow in real terms to exceed cut-offs. Therefore, small enterprises may face a higher reporting burden owing simply to price changes. Second, industry consolidation may increase the share of economic activity of the largest firms. Therefore, it may be possible to increase thresholds without significant loss of coverage. 

        Compilers must consider resources when using cut-off sampling.  Lower cut-offs reduce the amount of data that must be estimated. Since more enterprises are required to respond, a greater share of the estimates are actual data from reports. Estimates with lower cut-offs are likely to be more precise. However, lower cut-offs also impose a number of costs on both respondents and the compiling agency. Requiring more firms to respond imposes a burden on respondents since firm employees must devote time and effort to gathering information and filling out forms (and these costs are proportionally higher at small firms compared with large firms). Once a report is received, it requires substantial effort by editors to process. Processing more reports is more costly and slows the issuance of data. Cut-off sampling significantly reduces the costs of a survey by reducing the number of both reports and items filled out and processed.